PathRTM: Real-time prediction of KI-67 and tumor-infiltrated lymphocytes

TL;DR

PathRTM is a deep learning model that improves the accuracy and efficiency of estimating KI-67 proliferation and tumor-infiltrated lymphocytes in cancer diagnostics, with the ability to predict cell sizes using bounding boxes.

Contribution

We introduce PathRTM, a novel deep neural network that extends RTMDet with higher-level supervision for better estimation of cancer-related cellular markers.

Findings

Achieves 41.3% average precision in detection tasks

Outperforms previous methods in accuracy and runtime

Enables cell size estimation through bounding box predictions

Abstract

In this paper, we introduce PathRTM, a novel deep neural network detector based on RTMDet, for automated KI-67 proliferation and tumor-infiltrated lymphocyte estimation. KI-67 proliferation and tumor-infiltrated lymphocyte estimation play a crucial role in cancer diagnosis and treatment. PathRTM is an extension of the PathoNet work, which uses single pixel keypoints for within each cell. We demonstrate that PathRTM, with higher-level supervision in the form of bounding box labels generated automatically from the keypoints using NuClick, can significantly improve KI-67 proliferation and tumorinfiltrated lymphocyte estimation. Experiments on our custom dataset show that PathRTM achieves state-of-the-art performance in KI-67 immunopositive, immunonegative, and lymphocyte detection, with an average precision (AP) of 41.3%. Our results suggest that PathRTM is a promising approach for accurate KI-67 proliferation and tumor-infiltrated lymphocyte estimation, offering annotation efficiency, accurate predictive capabilities, and improved runtime. The method also enables estimation of cell sizes of interest, which was previously unavailable, through the bounding box predictions.